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1 Exchange Rate Predictability: Fundamentals versus Technical Analysis Ibrahim Jamali Ehab Yamani Associate Professor of Finance Visiting Assistant Professor of Finance American University in Beirut Jackson State University [email protected] [email protected] +1 (817) 673-6883 January 17, 2018 Abstract This paper compares the predictive ability of macroeconomic variables with that of technical indicators in generating out-of-sample forecasts for exchange rate returns. We use four measures for the macroeconomic variables based on the standard theories of exchange rate determination: uncovered interested rate parity, purchasing power parity, monetary fundamentals, and Taylor rule. We also use three popular trend following technical trading strategies in foreign exchange markets, namely, simple moving averages (MA) indicator, momentum (MOM) oscillator, and relative strength index (RSI). JEL classification: G14 G15 F31 Keywords: Exchange Rate Predictability; Forecasting; Fundamentals; Technical Trading.

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1

Exchange Rate Predictability: Fundamentals versus Technical Analysis

Ibrahim Jamali Ehab Yamani

Associate Professor of Finance Visiting Assistant Professor of Finance

American University in Beirut Jackson State University

[email protected] [email protected]

+1 (817) 673-6883

January 17, 2018

Abstract

This paper compares the predictive ability of macroeconomic variables with that of technical

indicators in generating out-of-sample forecasts for exchange rate returns. We use four measures

for the macroeconomic variables based on the standard theories of exchange rate determination:

uncovered interested rate parity, purchasing power parity, monetary fundamentals, and Taylor rule.

We also use three popular trend following technical trading strategies in foreign exchange markets,

namely, simple moving averages (MA) indicator, momentum (MOM) oscillator, and relative

strength index (RSI).

JEL classification: G14 G15 F31

Keywords: Exchange Rate Predictability; Forecasting; Fundamentals; Technical Trading.

2

Exchange Rate Predictability: Fundamentals versus Technical Analysis

Abstract

This paper compares the predictive ability of macroeconomic variables with that of technical

indicators in generating out-of-sample forecasts for exchange rate returns. We use four measures

for the macroeconomic variables based on the standard theories of exchange rate determination:

uncovered interested rate parity, purchasing power parity, monetary fundamentals, and Taylor rule.

We also use three popular trend following technical trading strategies in foreign exchange markets,

namely, simple moving averages (MA) indicator, momentum (MOM) oscillator, and relative

strength index (RSI).

JEL classification: G14 G15 F31

Keywords: Exchange Rate Predictability; Forecasting; Fundamentals; Technical Trading.

1. Introduction

Predicting exchange rate movements is undoubtedly a daunting task. Several anomalies, which

characterize the state of international finance, exacerbate the difficulty in predicting exchange

rates. First, the literature establishes the existence of a ‘disconnect’ between macroeconomic (and

monetary) fundamentals and exchange rate movements (Bacchetta and van Wincoop, 2006; Sarno

and Taylor, 2002; Evans and Lyons, 2002). The ‘exchange rate disconnect’ puzzle implies that

exploiting the informational content of fundamentals does not yield forecast improvements vis-à-

vis the random walk which Meese and Rogoff (1983) show is a stringent benchmark for assessing

the out-of-sample predictability in exchange rate changes.1

Second, the existing literature provides ample evidence against Uncovered Interest Parity

(UIP). The absence of empirical support for UIP, according to which exchange rate changes should

be equal to the interest rate differential between two countries, is closely connected to the ‘forward

premium puzzle’ which is a another widely researched anomaly in international finance. Under

1 A more positive assessment of the predictive power of fundamentals is provided in Engel, Mark and West (2007)

and Li, Tsiakas and Wang (2015). Engel, Mark and West (2007) argue that the random walk is an unnecessarily

stringent benchmark against which to compare the predictive ability of fundamentals.

3

risk neutrality and rational expectations, one of the implications of UIP is that the forward rate is

an unbiased predictor of the future spot rate (Li, Tsiakas and Wang, 2015). Nonetheless, the

considerable evidence on the ‘forward premium puzzle’ for the currencies of developed economies

implies that forward rates are biased predictors of the future spot rate.2

Perhaps the best assessment of the state of the exchange rate predictability literature is given

in Della Corte and Tsiakas (2012). The authors argue that, since Meese and Rogoff (1983), the

literature has come ‘full circle’ from finding no predictability, to uncovering predictability at long

horizons (Mark, 1995) and then back to failing to find evidence of predictability in currency

exchange rates (Cheung, Chinn and Pascual, 2005). After coming full circle, more recent

contributions to the literature provide compelling evidence of predictability in exchange rate

movements at short horizons (Molodtsova and Papell, 2009; Li, Tsiakas and Wang, 2015;

Anatolyev, Gospodinov, Jamali and Liu, 2017).

The presence of short-horizon predictability in exchange rate movements is consistent with the

widespread popularity of technical analysis among currency traders. At its core, technical trading

attempts to discern and exploit trends in asset prices. There have been several studies that examined

the profitability of technical trading strategies, such as Gencay (1999), LeBaron (1999), Lee et al.

(2001), Neely and Weller (2013), Raza et al. (2014), Katusiime et al. (2015), and Zarrabi et al.

(2017). While traders have long used technical trading rules in the foreign exchange market,

academic research provides somewhat mixed evidence regarding the efficacy and profitability of

technical analysis. Neely, Weller and Dittmar (1997) and Neely and Weller (2001) employ the

genetic programming algorithm to identify technical trading rules which generate economically

significant out-of-sample profitability in the foreign exchange market. Both studies report

2 For comprehensive reviews of the forward premium anomaly literature, see Engel (1996, 2015).

4

supportive evidence of technical trading rules’ ability to generate out-of-sample profits.3 Gençay

(1999) provides evidence that simple technical trading rules outperform the random walk out-of-

sample. In contrast, Neely and Weller (2003) find that accounting for transaction costs erodes the

profitability of technical trading rules when high-frequency data on four currencies are employed.4

In a thorough review of the literature, Park and Irwin (2007) synthesize the conclusions of recent

studies as being supportive of the profitability of technical analysis in foreign exchange and equity

markets.5 The profitability of technical trading rules is not surprising in light of the recent

contribution by Levine and Pedersen (2016) who show that the moving average crossovers, which

are a popular technical indicator, as well as other filters, are capable of detecting time series

momentum.

This paper examines the predictive power of fundamentals, technical indicators as well as high

frequency measures of risk and commodity prices in predicting the currency rate movements of

developing countries. Such an exploration contributes to the literature along several lines. First,

the existing literature examines the predictive power of fundamentals for the developed countries’

currencies. However, to the best of our knowledge, no such exploration is systematically

undertaken for developed countries currencies. In fact, the literature’s findings concerning the

predictive power of fundamentals need not generalize to developing countries’ currencies. For

3 Sweeney (1986) provides evidence of the effectiveness of technical analysis in foreign exchange market. Neely and

Weller (2001) find that exploiting information on the Federal Reserve’s intervention in the currency market enhances

the profitability of the trading rules identified via the genetic programming algorithm. In a related paper, Neely (2000)

finds that the profitability of trading rules cannot be ascribed solely to central bank intervention. Rather, the author

provides evidence that technical trading rule are profitable before the central bank intervention. 4 The conclusions from the strand of research examining linear and non-linear implementations of technical analysis

in equity markets (Sweeney, 1988; Gençay 1996, 1998; Gençay and Stengos, 1998; Neely, Rapach, Tu and Zhou,

2014) lean towards uncovering out-of-sample profitability. However, a number of studies (see, for example, Ready,

2002; Bessembinder and Chang, 1998) starting with Sullivan, Timmermann and White (2000) argue that researchers

should be mindful of data measurement and, more importantly, data snooping biases before drawing such a conclusion. 5 However, the authors themselves do not find favorable results from applying technical trading rules to U.S. futures

(Park and Irwin, 2010).

5

instance, while the forward premium anomaly is a staple of the developing countries’ currencies,

existing research (Bansal and Dahlquist, 2000; Frankel and Poonawala, 2010) suggests that the

forward premium puzzle is much less pronounced for the currencies of developing economies.

This, in turn, implies that while the forward rate may still be a biased predictor of future exchange

rate movements of emerging markets, it will indicate the correct directional change in the exchange

rate movements (Frankel and Poonawala, 2010).6

Second, this paper is the first to examine the predictive ability of technical indicators for

emerging market economies. The lower turnover in emerging market currencies (Bank of

International Settlements, 2016) and the lesser competition among traders might imply that

technical trading indicators generate profitable signals. This latter view is echoed in Frankel and

Poonawala (2010) who assert that “Emerging market currencies probably have more easily-

identified trends of depreciation than currencies of advanced countries”.

The remainder of the paper is organized as follows. Data and variables are set forth in section

2. Section 3 examines the in-sample analysis of exchange rate returns using both fundamental and

technical indicators. Section 4 presents out-of-sample forecasts. Section 5 concludes.

2. Data and Variables

2.1. Spot and one-month forward rates

We collect the WMR/Reuters spot and one-month forward exchange rates for a cross-section

of twenty-three developing and developed economies against the United States Dollar (USD).

Thirteen countries in our sample are classified by the World Bank and the International Monetary

Fund as developing whereas ten are developed countries. More specifically, our cross-section

6 It is interesting to note, in this context, that Bansal and Dahlquist (2000) relate the attenuation of the forward premium

puzzle for emerging market economies to macroeconomic fundamentals such as per capita GNP, average inflation

rates and inflation volatility. This implies that fundamentals might possess predictive power for the exchange rate

changes of developing countries.

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comprises the following developing country currencies: the Mexican New Peso (MXN), Hong

Kong Dollar (HKD), Indian Rupee (INR), Indonesian Rupiah (IDR), Philippine Peso (PHP),

Kuwaiti Dinar (KWD), New Taiwan Dollar (TWD), Saudi Riyal (SAR), Singapore Dollar (SGD),

Thai Baht (THB), Czech Koruna (CZK), Hungarian Forint (HUF) and South African Rand

(ZAR).7 In order to benchmark our results against those of influential studies in the literature (Della

Corte and Tsiakas, 2012; Li, Tsiakas and Wang, 2015; Burnside et al., 2011a; Lustig et al., 2011;

Daniel et al., 2017; Bekaert and Panayotov, 2016), we also provide results for the ten most liquid

currencies of developed countries in the world. The G10 currencies we include in our cross-section

are: British pound (GBP), Canadian dollar (CAD), Swiss franc (CHF), Euro (EUR), Japanese yen

(JPY), Australian dollar (AUD), New Zealand dollar (NZD), Swedish krona (SEK) and Norwegian

krone (NOK).

Our data spans the period from December 1996 to June 2017. Our starting date is dictated by

the availability of one-month forward rate data for the developing currencies while our sample is

contained to end in June 2017 given that Gross Domestic Product (GDP) (see section 2.2) data are

available with a time lag. The monthly spot and one-month forward quotes are sample from daily

data as the last observation of the month. The returns on currency i in month t is given by: ∆𝑠𝑖𝑡 =

ln(𝑆𝑖𝑡) − ln(𝑆𝑖𝑡−1) = 𝑠𝑖𝑡 − 𝑠𝑖𝑡−1 for 𝑖 = 1,2, … ,23 where itS denotes the exchange rate expressed

in terms of US dollar price of a unit of the foreign currency. The return on currency i is the

dependent variable in our predictive models.

7 Hong Kong, Thailand, and Saudi Arabia have pegged their currencies to the USD during part of our sample period.

However, we elect to keep them in our cross-section, as in Verdelhan (2017) and Lustig, Roussanov, and Verdelhan

(2011), because their forward prices are not inconsistent with covered interest rate parity.

7

2.2. Macroeconomic data

We obtain macroeconomic data for the twenty-three countries comprising our cross-section

from Datastream. More specifically, estimation and prediction from the models with fundamentals

requires data on GDP, inflation and the money supply of each of the countries. We collect data on

the seasonally adjust GDP and non-seasonally adjusted M1 as a measure of the money supply and

Consumer Price Index (CPI) of each of the countries.8 As noted in the online appendix, the source

of the macroeconomic data are the central bank and national statistical agencies for each of the

countries. In specific, the money supply data are collected by the central bank while the source of

the GDP and CPI data are the national statistical agencies of each of the countries.

Following Della Corte and Tsiakas (2012), we deseasonalize M1 by implementing the

procedure of Gomez and Maravall (2000). We also use Gomez and Maravall (2000)’s approach to

seasonally adjust the CPI for each of the countries. Given that GDP is only available at the

quarterly frequency (for all the countries), we linearly interpolate the GDP series from to obtain

data at the monthly frequency using the Chow and Lin (1971) procedure.9 GDP and M1 figures

are expressed in USD using the spot exchange rate against the USD. The CPI data for New Zealand

and Australia are available only at the quarterly frequency so we linearly interpolate the CPI for

the latter two countries using the Chow and Lin (1971) procedure.

We should highlight some important data gaps for the developing economies. Data on the CPI

are not available for India and Kuwait and Saudi Arabia while we could not obtain GDP data for

8 The Datastream mnemonic for each of the series can be found in the online appendix. 9 We closely follow Table 1 of Della Corte and Tsiakas (2012) when collecting the M1 data for the developed

countries. However, we opt not to reply on the industrial production indexes as they do. Our choice is driven by the

fact that the industrial production data are not available for many of the developing countries. Given that we would

like to compare the predictive performance of the different models for developing and developed countries on an equal

footing, we instead use GDP numbers for the developed economies and interpolate these. The same considerations

drive us to use the national statistical agencies’ CPI indexes for all the countries instead of relying on the OECD CPI

data used in Della Corte and Tsiakas (2012). The OECD data are available only for the developed economies.

8

India. The GDP data for Indonesia are available only starting in 2011:Q2 while those of the

Philippines and Turkey are available only starting 1998:Q1. The M1 data for Thailand are available

only starting November 2015 while those of Turkey are available starting December 2005. In light

of these data constraints, we are unable to estimate and predict from some of the models with

fundamentals for these currencies.

3. Econometric Methodology

This section describes the modelling approach that we follow. Throughout our analysis, the

returns on currency i in month t is the dependent variable in our predictive regression models, and

given by the change in the log of spot exchange rate (∆𝑠𝑖𝑡+1 = 𝑙𝑛(𝑆𝑖𝑡+1) − 𝑙𝑛(𝑆𝑖𝑡)) for 𝑖 =

1,2,3, … . ,23, where 𝑆𝑖𝑡 denotes the nominal spot exchange rate expressed in terms of US dollar

price of a unit of the foreign currency. We initially present the predictive regression models we

use to empirically examine the predictive power of fundamentals and technical indicators in

predicting the currency rate movements, ∆𝑠𝑖𝑡+1, and then we describe the statistical procedures we

use to evaluate our predictive regression models against the benchmark random walk (RW) model.

3.1. The Predictive Power of Fundamentals

Since the seminal contribution of Meese and Rogoff (1983), the RW model constitutes the

benchmark against which the statistical accuracy of exchange rate forecasting models is assessed.

Like Della Corte and Tsiakas (2012), most of our predictive models are cast within the general

framework of a predictive regression. The simple predictive regression is given by:

∆𝑠𝑖𝑡+1 = 𝛼 + 𝛽𝑥𝑖𝑡 + 𝜀𝑖𝑡+1, (1)

where itx denotes one of the fundamental or technical predictors. We provide next an overview of

each of the models we employ. The exposition we adopt next follows Della Corte and Tsiakas

(2012) closely. It is noteworthy that the random walk with drift benchmark used in foreign

9

exchange rate markets is equivalent to assessed the historical average model employed as a

benchmark in equity markets (Welch and Goyal, 2008; Neely, Rapach, Tu, and Zhou, 2014).

3.1.1. Random Walk with Drift

The first model that we consider is the random walk with drift. Since the seminal contribution

of Meese and Rogoff (1983), the random walk constitutes the benchmark against which the

statistical accuracy of exchange rate forecasting models is assessed. As Della Corte and Tsiakas

(2012) note, imposing 𝛽 = 0 yields the random walk with drift model under which ∆𝑠𝑖𝑡+1 = 𝛼 +

𝜖𝑖𝑡+1. The random walk with drift benchmark used in foreign exchange rate markets is equivalent

to assessed the historical average model employed as a benchmark in equity markets (Welch and

Goyal, 2008; Neely, Rapach, Tu and Zhou (2014).

3.1.2 Uncovered and Covered Interest Parity

Under the assumptions that agents are risk-neutral and form rational expectations, Uncovered

Interest Parity (UIP) postulates the exchange in the exchange rate is equal to the currency

differential between two economies (Sarno and Taylor, 2002). More formally, let 𝑖𝑡 and 𝑖𝑡∗ denote,

respectively, the nominal interest rates on comparable domestic and foreign securities. If UIP

holds, the change in the exchange rate should be equal to the interest rate differential ∆𝑠𝑡+1 = 𝑖𝑡 −

𝑖𝑡∗. UIP is one of the most researched hypotheses in modern international finance. UIP can be tested

using the predictive regression ∆𝑠𝑖𝑡+1 = 𝛼 + 𝛽(𝑖𝑡 − 𝑖𝑡∗) + 𝜗𝑡+1. If UIP holds, the null hypothesis

1,0:0 H cannot be rejected. The UIP hypothesis can also be tested indirectly by imposing

Covered Interest Parity (CIP). CIP is another widely researched hypothesis in international

finance, which stipulates that the interest rate differential between the two currencies is equal to

the forward premium. More formally, the CIP hypothesis is given, in logarithmic form, by 𝑓𝑡 −

𝑠𝑡 = 𝑖𝑡 − 𝑖𝑡∗ where 𝑓𝑡 = ln (𝐹𝑡) is the logarithm of the one-month forward rate and 𝑓𝑡 − 𝑠𝑡 is the

10

forward premium (or discount). Unlike UIP, which is a predictive relation, CIP is a

contemporaneous arbitrage relation with ample empirical support.10 When combined with CIP,

UIP can be tested via the Fama (1984) regression: 11

𝑥𝑖𝑡 = 𝑓𝑖𝑡 − 𝑠𝑖𝑡 . (2)

Under UIP, the null hypothesis 1,0:0 H is not rejected.

Li, Tsiakas and Wang (2015) note that the implications of rejecting UIP are twofold. The first

is that the forward rate is a biased predictor of the future spot rate. The sizeable literature on the

forward premium anomaly for developed currencies provides empirical evidence, which confirms

that the forward rate is a biased predictor of the future spot rate. While abundant empirical evidence

against UIP exists for the currencies of developed currencies, existing studies (Bansal and

Dahlquist, 2000; Frankel and Poonawala, 2010) indicate that the anomaly is much less pronounced

for the currencies of developing economies. This potentially makes the forward rate a useful

predictor of the future spot rate. The second implication of rejecting UIP is that the exchange rate

change is not equal to the interest rate differential.

3.1.3. Purchasing Power Parity

A Purchasing Power Parity (PPP) exchange rate guarantees that a unit of the currency has the

same purchasing power in two economies (Sarno and Taylor, 2002).12 As Mark (2001) notes, the

10 CIP is typically tested using the regression 𝑓𝑡 − 𝑠𝑡 = 𝛼 + 𝛽(𝑖𝑡 − 𝑖𝑡

∗) + 𝜖𝑡. If CIP holds, the null hypothesis 𝐻0: 𝛼 =0, 𝛽 = 1 should not be rejected. For studies providing empirical support for at high frequencies prior to the financial

crisis see, for example, Akram, Rime and Sarno (2008) and Fong, Valente, Fung (2010). Recent contributions to the

literature provide evidence of short-lived deviations from CIP during and after the financial crisis (Baba and Packer,

2009; Du, Tepper, and Verdelhan, 2016; Borio, McCauley, McGuire, Sushko, 2016; Mancini Griffoli and Ranaldo,

2011). 11 Studies which use the Fama (1984) approach include Froot and Thaler (1990), Baillie and Bollerslev (1989,2000),

Bansal and Dahlquist (2000), Frankel and Poonawala (2010) and Ahmad, Rhee and Wong (2012). 12 A more elaborate and accurate statement of a PPP exchange rate is one which “would equate the two relevant

national price levels if expressed in a common currency” (Sarno and Taylor, 2002).

11

commodity-arbitrage view of PPP in Samuelson (1964) requires that the Law of One Price (LOOP)

hold for tradeable goods. The PPP hypothesis can be tested using the regression:

𝑥𝑖𝑡 = 𝑝𝑖𝑡 − 𝑝𝑖𝑡∗ − 𝑠𝑖𝑡 , (3)

where 𝑝𝑡 and 𝑝𝑡∗ denote, respectively, the logarithm of the domestic and foreign price levels.

While PPP is commonly viewed as a long-run condition, existing studies (Della Corte and

Tsiakas, 2012; Li, Tsiakas and Wang, 2015) explore its short-run predictive performance for the

developed economies’ currencies. The findings emerging from the literature cast doubt on the

predictive ability of PPP. In the context of emerging markets, Taylor and Taylor (2004) provide

evidence of long-lived deviations from PPP. This is likely to translate to a weak predictive

performance of PPP for the emerging markets’ currencies.

3.1.4. Monetary Fundamentals

International macroeconomic models postulate that nominal exchange rate movements are

driven by macroeconomic fundamentals. More specifically, the monetary fundamentals model is

given by:

𝑥𝑖𝑡 = (𝑚𝑡 − 𝑚𝑡∗) − (𝑦𝑡 − 𝑦𝑡

∗) − 𝑠𝑡 , (4)

where 𝑚𝑡 and 𝑚𝑡∗ denote, respectively, the logarithms of the domestic and foreign money supply

and 𝑦𝑡 and 𝑦𝑡∗ are, respectively, the natural logarithms of the domestic and foreign national income.

Existing research documents a feeble link (Sarno and Sojli, 2009) between exchange rate

movements and fundamentals. Several explanations have been offered for the weak relation

between fundamentals and exchange rate returns. Engel and West (2005) show analytically that

the disconnect between fundamentals and exchange rates can be driven by a discount factor that is

close to unity. Sarno and Sojli (2009) provide empirical evidence that the discount rate is close to

one and thereby support Engel and West (2005)’s analytical account. Another explanation of the

12

disconnect between fundamentals and exchange rates is offered by Sarno and Valente (2009) who

argue that the relationship between fundamentals and exchange rates is shifting across time.13

Despite that monetary fundamentals do not exhibit predictive power for the developed currencies,

existing studies do not explore the predictive power of monetary fundamentals for the developing

currencies.

3.2. The Predictive Power of Technical Trading Indicators

In contrast to fundamentalists, technicians rely upon technical indicators (or trading rules) to

construct forecast of FX returns since they expect a gradual price adjustment to reflect the gradual

flow of information, which causes trends in the currency price movements. We use the following

predictive regression model to analyze FX predictability based on technical trading indicators

under the null hypothesis of no predictability(𝛽𝑇𝑅 = 0):

∆𝑠𝑖𝑡+1 = 𝛼 + 𝛽𝑇𝑅𝑧𝑖𝑡 + 𝜀𝑖𝑡+1 (5)

where 𝑧𝑖𝑡 represents a buy (bullish) or sell (bearish) signal (𝑧𝑖𝑡 = 1 or 𝑧𝑖𝑡 = 0, respectively)

generated from technical trading rules at the end of month t. To this end, we use three popular

technical trading rules in FX markets, namely, simple moving averages (MA) indicator,

momentum (MOM) oscillator, and relative strength index (RSI).

3.2.1. Moving Average

We define both buy and sell signals for each trading rule. For the MA trading rule, the monthly

simple moving average of the past spot exchange rates is measured over the last 9 months

(𝑖. 𝑒., 𝑀𝐴𝑡 = 19⁄ ∑ 𝑆𝑡−𝑖

𝑀−1𝑖=0 ) for each currency pair.14 An upward (downward) trend is usually

13 Yet another potential explanation of the feeble link between fundamentals and exchange rate changes is

nonlinearities. Sarno, Valente and Leon (2006) provide evidence of nonlinearities in UIP. 14 The 1-month/9-month MA strategy is very commonly used by currency traders and by many academic scholars

(e.g., Gencay, 1999). Further, LeBaron (1999) shows that trading rule profitability is not overly sensitive to the actual

length of the moving average.

13

identified when the spot rate is greater (less) than the moving average. Buy and sell signals are

thus defined as

𝑀𝐴 𝑇𝑟𝑎𝑑𝑖𝑛𝑔 𝑆𝑖𝑔𝑛𝑎𝑙𝑠: {𝑍𝑡

𝑀𝐴 = 1, 𝑖𝑓 𝑆𝑡 > 𝑀𝐴𝑡 (𝐵𝑢𝑦 𝑆𝑖𝑔𝑛𝑎𝑙)

𝑍𝑡𝑀𝐴 = 0, 𝑖𝑓 𝑆𝑡 ≤ 𝑀𝐴𝑡 , (𝑆𝑒𝑙𝑙 𝑆𝑖𝑔𝑛𝑎𝑙)

} (6)

3.2.2. Momentum

The MOM strategy measures the amount that spot exchange rate for a currency has changed

over a given time span. We calculate the momentum as a ratio of the price of the spot exchange

rate in the current month to the price 9 months ago, and then utilize the following buy and sell

signals

𝑀𝑂𝑀 𝑇𝑟𝑎𝑑𝑖𝑛𝑔 𝑆𝑖𝑔𝑛𝑎𝑙𝑠: {𝑍𝑡

𝑀𝑂𝑀 = 1, 𝑖𝑓 𝑆𝑡 > 𝑆𝑡−9

𝑍𝑡𝑀𝑂𝑀 = 0, 𝑖𝑓 𝑆𝑡 ≤ 𝑆𝑡−9

} (7)

3.2.3. Relative Strength Index

The RSI indicator focuses on total gain or loss in previous market days rather than prior price

movements as the MA and MOM indicators.15 To get a first indication to these signals over the

three sub-sample periods, The RSI identifies four thresholds: the RSI bottoms below 30 indicating

that the falling market trend is likely to reverse and thus suggesting a bullish signal, and tops above

70 indicating that the resistance level for the currency pair is near or has been reached and thus

suggesting a bearish signal. Buy and sell signals are thus defined as

𝑅𝑆𝐼 𝑇𝑟𝑎𝑑𝑖𝑛𝑔 𝑆𝑖𝑔𝑛𝑎𝑙𝑠: {𝑍𝑡

𝑅𝑆𝐼 = 1, 𝑖𝑓 𝑅𝑆𝐼𝑡 < 30

𝑍𝑡𝑅𝑆𝐼 = 0, 𝑖𝑓 𝑅𝑆𝐼𝑡 > 70

} (8)

3.3. Statistical Evaluation of Predictive Regressions

15 We calculate first the monthly relative strength 𝑅𝑆 measured as the ratio of total average gains to total average

losses (𝑅𝑆 = 𝐴𝑣. 𝐺𝑎𝑖𝑛𝑠 𝐴𝑣. 𝐿𝑜𝑠𝑠𝑒𝑠⁄ ) for each currency pair. Average gains (losses) are calculated by totaling all

gains (losses) from the past 14 months and dividing by 14, where monthly gain (loss) is determined if the spot rate in

the current month is higher (lower) than the previous month’s spot rate. The RS is then converted to an index value

that ranges between 0 and 100, using the following equation: 𝑅𝑆𝐼 = 100 − [100/(1 + 𝑅𝑆].

14

We first run out-of-sample (OOS) monthly forecasts by estimating our predictive regression

models using fundamental variables (∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑥𝑖𝑡) as well as technical indicators(∆�̂�𝑖𝑡+1 =

�̂� + �̂�𝑧𝑖𝑡), where �̂� and �̂� are the OLS estimates computed from regressing currency returns on a

constant and one of our predictive regressors. We obtain the OOS monthly forecasts using rolling

regressions by restimating the model parameters every time we increase the beginning and ending

dates by a new monthly observation, using a fixed window equal to 140 observations. More

specifically, our first OOS one month ahead forecast is for September 2008, using an IS period

from January 1997 to August 2008, and our last OOS forecast is for August 2017, using an IS

period from August 2006 to July 2017. This exercise produces 108 OOS forecasts.16

We then evaluate the out-of-sample predictive power of the above empirical exchange rate

models by comparing the forecasts given by the fundamental-based regressors (in equation 1) and

the technical indicators (in equation 6) against the benchmark random walk model (in equation 9).

The comparative performance of the models is assessed using two statistics commonly used in the

literature: the out-of-sample R-square of Campbell and Thompson (2008), and the mean squared

forecast error (MSFE) adjusted statistic of Clark and West (2007). First, the Campbell and

Thompson (2008) out-of-sample R-square (𝑅𝑜𝑜𝑠2 ) measures the proportional reduction in MSFE

for the one-month ahead conditional forecasts (∆�̂�𝑡+1|𝑡) using both fundamental and technical

indicators (i.e., where 𝛽 ≠ 0), relative to the one-month ahead unconditional forecast

(∆�̅�𝑡+1|𝑡) using the random walk model (i.e., where 𝛽 = 0). Mathematically, the 𝑅𝑜𝑜𝑠2 is given by

𝑅𝑜𝑜𝑠2 = 1 −

𝑀𝑆𝐹𝐸(∆�̂�𝑡+1|𝑡)

𝑀𝑆𝐹𝐸(∆�̅�𝑡+1|𝑡) (9)

16 Our IS sample period is comparable to Li, Tsiakas, and Wang (2015) who use 11 year time horizon as IS period.

The motivation for choosing August 2008 as the ending date of our IS period is that a number of previous studies

(e.g., Li, Tsiakas, and Wang, 2015; Buncic and Piras, 2016) find that there is a change in the predictability in the pre

Lehman Brothers collapse period compared to the Lehman collapse period which started in September 2008.

15

A positive (negative) 𝑅𝑜𝑜𝑠2 thus indicates that the predictive regression forecast model outperform

(underperform) the benchmark RW model. Second, the Clark and West statistic tests the null

hypothesis that the 𝑅𝑜𝑜𝑠2 is less than or equal to zero, so that MSFE of the RW model is less than

or equal MSFE of the alternative model.

4. Empirical Results

A natural starting point for our analysis is a comparison of exchange rate changes across

developed and emerging countries. Table 1 presents descriptive statistics for the study variables:

currency returns, ∆𝑠𝑖𝑡+1; interest rate differentials, 𝑖𝑖𝑡 − 𝑖𝑖𝑡∗ ; national price level differential, 𝑝𝑖𝑡 −

𝑝𝑖𝑡∗ ; money supply differentials, 𝑚𝑖𝑡 − 𝑚𝑖𝑡

∗ ; and real output differentials, 𝑦 − 𝑦𝑖𝑡∗ .

Table 2 reports the one-step ahead out-of-sample forecast evaluation results of six empirical

exchange rate models against the null of a random walk (RW) using data from developed countries.

We assess the statistical ability of 3 fundamental indicators (UIP, PPP, and MF) and 3 technical

indicators (MA, MOM, and RSI) in predictive currency returns, by reporting out-of-sample tests

of predictability against the null of the RW. We focus on the Campbell and Thompson (2008) out-

of-sample R-square, and the Clark and West (2007) Mean Squared Forecast Error (MSFE)

adjusted t-statistic. Table 3 presents the out-of-sample forecast results for developing countries.

5. Robustness Tests

As a robustness check, this section reports OOS forecasting statistics using recursive

regression for the fundamental and technical indicators, as well as the in-sample forecasting.

5.1. Recursive Estimation

We obtain the out-of-sample monthly forecasts using recursive regressions by restimating the

model parameters every time a new monthly observation is added to the sample. Similar to our

rolling regression procedure, this exercise produces 108 OOS forecasts. Our first OOS forecast is

16

for September 2008, using IS period from January 1997 to August 2008, and our last OOS forecast

is for August 2017, using IS period from January 1997 to July 2017. Tables 4 and 5 reports the

recursive regression results for developed and developing countries, respectively.

5.2. In-Sample Estimation

The comparative performance of the models is assessed for the entire sample of December

1996 to August 2017. We can follow Neely, Rapach, Tu and Zhou by testing whether the slope

coefficient in the regression is positive and significant. We use our full sample period (from

December 1996 to August 2017) for in-Sample forecasting. Table 4 reports in-sample estimation

results. The R2-statistics are computed for the initial in-sample estimation period spanning from

December 1996 to December 2014. The last row in the table shows the panel estimation results

for the pooled sample. Table 4 presents the OLS estimates. We focus primarily on the significance

of the slope estimate of the predictive regressions

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19

Table 1: Descriptive Statistics

This table reports summary statistics for the log spot exchange rate changes (i. e., 𝑟𝑖𝑡+1 = log(St+1) − log(St)), where 𝑆𝑡 is the spot exchange rate of the

foreign currency against the USD, and 4 macroeconomic variables for the full sample period spanning from December 1996 to July 2017. We use a sample

of 13 developing countries: Mexican New Peso (MXN) (from Latin America); Hong Kong Dollar (HKD), Indian Rupee (INR), Indonesian Rupiah (IDR),

Philippine Peso (PHP), Kuwaiti Dinar (KWD), New Taiwan Dollar (TWD), Saudi Riyal (SAR), Singapore Dollar (SGD), Thai Baht (THB) (from Asia); Czech

Koruna (CZK), Hungarian Forint (HUF) (from Emerging Europe); South African Rand (ZAR) (from Africa).

Panel A: Developed Currencies

∆𝑠𝑡+1 ∆(𝑝 − 𝑝∗) ∆(𝑚 − 𝑚∗) ∆(𝑦 − 𝑦∗)

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

GBP -0.104 2.480 0.216 1.273 -0.152 2.833 0.020 0.405

CAD 0.014 2.530 0.302 0.791 -0.221 2.660 -0.016 0.412

CHF 0.124 3.049 1.670 0.693 -0.177 3.290 0.031 0.412

EUR -0.039 2.881 0.431 0.667 -0.115 5.694 0.065 0.420

JPY 0.005 3.123 2.015 1.494 -0.025 3.305 0.130 0.586

AUD -0.024 3.626 -0.308 1.263 -0.046 4.002 -0.064 0.529

NZD 0.011 3.809 0.158 1.189 -0.462 6.657 -0.035 0.607

SEK -0.092 3.188 1.064 1.078 0.080 3.812 -0.021 0.545

NOK -0.108 3.245 0.038 1.469 -0.064 5.882 0.027 0.657

Panel B: Developing Currencies

∆𝑠𝑡+1 ∆(𝑝 − 𝑝∗) ∆(𝑚 − 𝑚∗) ∆(𝑦 − 𝑦∗)

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

MXN -0.335 2.904 -4.141 4.779 -0.358 3.076 -0.017 0.356

HKD -0.003 0.120 0.466 2.829 -0.501 7.040 -0.080 0.577

INR -0.240 2.037 -7.049 10.502 -0.286 3.453 - -

IDR -0.703 7.403 - - -0.067 6.704 - -

PHP -0.264 2.445 -2.336 1.980 -0.387 3.053 -0.220 0.395

KWD -0.006 0.706 - - -0.355 3.964 - -

TWD -0.041 1.593 1.149 1.254 -0.054 2.363 -0.131 0.651

SAR 0.000 0.086 - - -0.399 1.780 - -

SGD 0.007 1.780 0.600 1.845 -0.302 2.439 -0.214 0.825

THB -0.115 3.176 -0.367 1.931 - - - -

CZK 0.067 3.592 0.385 2.337 -0.207 3.747 0.022 0.758

HUF -0.217 3.894 -3.579 4.168 -0.362 3.800 -0.011 0.303

ZAR -0.423 4.573 -3.640 2.571 -0.074 5.104 -0.031 0.277

TRY -1.432 4.663 -19.198 19.739 -0.025 4.358 -0.174 4.483

20

Table 2: Out-of-Sample Forecasting Results using Rolling Regressions – Developed Countries

The table reports the out-of-sample 𝑅𝑜𝑜𝑠2 , McCracken (2007) MSE-F, and change in RMSE-statistics for the predictive regression models for the developed market

currencies, against the null of a random walk (RW). Panel A shows the results using fundamental indicators given by,

∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑥𝑖𝑡 , ∆�̂�𝑖𝑡+1 is one-step ahead forecast of the log spot exchange rate changes; 𝑥𝑖𝑡 is one of the 3 macroeconomic variables (based on UIP, PPP, and MF). Panel B presents

the results using technical trading indicators given by

∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑧𝑖𝑡

where 𝑧𝑖𝑡 is one of the 3 technical trading signals (based on MA, MOM, and RSI). We obtain the OOS monthly forecasts using rolling regressions using a fixed

window equal to 140 observations. This exercise produces 108 OOS forecasts, so that our first OOS forecast is for September 2008, using IS period from January

1997 to August 2008, and our last OOS forecast is for August 2017, using IS period from August 2006 to July 2017. The last row in each panel shows the estimates

for the pooled sample that includes all the sample currencies. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

Panel A: Fundamental Indicators

UIP PPP MF

𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠

2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE

Australia -0.011 -1.202 -0.024 0.012 1.336 0.026 -0.028 -2.901 -0.059 Canada 3.61E-04 0.038 5.520E-04 -0.018 -1.928 -0.028 -0.017 -1.806 -0.026

Euro 0.003 0.347 0.005 -0.025 -2.606 -0.040 -0.021 -2.241 -0.035 Japan 1.48E-04 0.015 2.28E-04 -0.007 -0.764 -0.011 0.021 2.306 0.033

New Zealand -0.025 -2.657 -0.057 0.017 1.845 0.038 -0.465 -33.664 -0.954 Norway -0.045 -4.647 -0.082 -0.077 -7.665 -0.139 -0.055 -5.568 -0.099 Sweden -0.011 -1.244 -0.021 -0.022 -2.365 -0.041 -0.012 -1.261 -0.021

Switzerland -0.003 -0.374 -0.005 0.007 0.832 0.013 7.32E-04 0.077 0.001 UK -0.010 -1.095 -0.014 -0.006 -0.666 -0.008 -0.023 -2.429 -0.033

21

Table 2: continued

Panel B: Technical Indicators

MA MOM RSI

𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠

2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE

Australia 0.167 21.399 0.374 0.167 8.251 0.156 -0.027 -2.793 -0.057 Canada 0.131 16.073 0.2083 0.131 5.139 0.071 0.029 3.269 0.046

Euro 0.108 12.856 0.181 0.108 1.935 0.029 0.041 4.542 0.067 Japan 0.166 21.222 0.268 0.166 7.070 0.098 -0.004 -0.521 -0.007

New Zealand 0.150 18.848 0.356 0.150 8.684 0.174 0.003 0.383 0.008 Norway 0.111 13.294 0.209 0.114 0.614 0.010 0.031 3.460 0.058 Sweden 0.147 18.399 0.278 0.147 6.593 0.107 0.057 6.408 0.104

Switzerland 0.185 24.071 0.328 0.185 0.189 0.003 0.041 4.622 0.071 UK 0.146 18.173 0.215 0.146 0.566 0.007 0.020 2.162 0.028

22

Table 3: Out-of-Sample Forecasting Results using Rolling Regressions – Developing Countries

The table reports the out-of-sample 𝑅𝑜𝑜𝑠2 , McCracken (2007) MSE-F, and change in RMSE-statistics for the predictive regression models for the developing market

currencies, against the null of a random walk (RW). Panel A shows the results using fundamental indicators given by,

∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑥𝑖𝑡 , ∆�̂�𝑖𝑡+1 is one-step ahead forecast of the log spot exchange rate changes; 𝑥𝑖𝑡 is one of the 3 macroeconomic variables (based on UIP, PPP, and MF). Panel B presents

the results using technical trading indicators given by

∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑧𝑖𝑡

where 𝑧𝑖𝑡 is one of the 3 technical trading signals (based on MA, MOM, and RSI). We obtain the OOS monthly forecasts using rolling regressions using a fixed

window equal to 140 observations. This exercise produces 108 OOS forecasts, so that our first OOS forecast is for September 2008, using IS period from January

1997 to August 2008, and our last OOS forecast is for August 2017, using IS period from August 2006 to July 2017. The last row in each panel shows the estimates

for the pooled sample that includes all the sample currencies. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. NA indicates not

applicable due to data availability during the sample period.

Panel A: Fundamental Indicators

UIP PPP MF

𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠

2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE

Philippine -0.028 -2.978 -0.023 -0.051 -5.182 -0.042 0.006 0.647 0.005 Chez Rep. 0.003 0.355 0.006 -0.027 -2.832 -0.052 -0.027 -2.799 -0.051 Hong Kong -0.001 -0.136 -7.61E-05 1.23E-04 0.013 7.29E-06 -0.005 -0.612 -3.42E-04 Indonesia -0.010 -1.138 -0.016 -0.089 -8.736 -0.134 NA NA NA

India -0.011 -1.254 -0.016 NA NA NA NA NA NA Kuwait -0.081 -7.990 -0.033 NA NA NA NA NA NA

Hungary -0.004 -0.457 -0.010 -0.036 -3.709 -0.086 -0.023 -2.412 -0.055 Mexico -0.007 -0.835 -0.014 -0.016 -1.721 -0.029 -0.037 -3.842 -0.067 Saudi -0.317 -25.553 -0.007 NA NA NA NA NA NA

Singapore 0.005 0.558 0.005 -0.019 -1.990 -0.018 -0.024 -2.500 -0.023 South Africa 0.011 1.184 0.026 -0.018 -1.940 -0.043 -5.13E-04 -0.054 -0.001

Taiwan 0.005 0.571 0.004 -0.014 -1.511 -0.010 -0.009 -1.036 -0.007 Thailand -0.034 -3.529 -0.027 0.003 0.409 0.003 NA NA NA Turkey 0.021 2.338 0.040 0.021 1.017 0.017 NA NA NA

23

Table 3: continued

Panel B: Technical Indicators

MA MOM RSI

𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠

2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE

Philippine 0.195 25.835 0.172 0.089 10.402 0.076 -0.028 -2.905 -0.023

Chez Rep. 0.150 18.710 0.300 0.048 5.414 0.094 -0.071 -7.044 -0.134

Hong Kong 0.142 17.584 0.008 0.052 5.926 0.003 0.137 16.967 0.008

Indonesia 0.141 17.537 0.225 0.005 0.638 0.009 -0.009 -1.026 -0.014

India

Kuwait

Hungary 0.124 15.053 0.310 0.028 3.081 0.068 -0.003 -0.342 -0.007

Mexico 0.113 13.623 0.211 0.044 4.878 0.080 0.026 2.924 0.048

Saudi

Singapore -180.67 -105.416 -0.655 -98.311 -104.932 -0.470 -28.917 -102.45 -0.234

South Africa 0.133 16.288 0.325 0.033 3.652 0.079 0.029 3.233 0.070

Taiwan 0.131 16.112 0.104 0.038 4.244 0.029 0.006 0.655 0.004

Thailand 0.162 20.589 0.137 0.0514 5.747 0.042 -0.023 -2.411 -0.018

Turkey 0.126 15.415 0.244 0.019 2.052 0.035 -0.005 -0.625 -0.011

24

Table 4: Out-of-Sample Forecasting Results using Recursive Regressions – Developed Countries

The table reports the out-of-sample 𝑅𝑜𝑜𝑠2 , McCracken (2007) MSE-F, and change in RMSE-statistics for the predictive regression models for the developed market

currencies, against the null of a random walk (RW). Panel A shows the results using fundamental indicators given by,

∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑥𝑖𝑡 , ∆�̂�𝑖𝑡+1 is one-step ahead forecast of the log spot exchange rate changes; 𝑥𝑖𝑡 is one of the 3 macroeconomic variables (based on UIP, PPP, and MF). Panel B presents

the results using technical trading indicators given by

∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑧𝑖𝑡

where 𝑧𝑖𝑡 is one of the 3 technical trading signals (based on MA, MOM, and RSI). We obtain the OOS monthly forecasts using rolling regressions using a fixed

window equal to 140 observations. This exercise produces 108 OOS forecasts, so that our first OOS forecast is for September 2008, using IS period from January

1997 to August 2008, and our last OOS forecast is for August 2017, using IS period from August 2006 to July 2017. The last row in each panel shows the estimates

for the pooled sample that includes all the sample currencies. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

Panel A: Fundamental Indicators

UIP PPP MF

𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠

2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE

Australia Canada

Euro Japan

New Zealand Norway Sweden

Switzerland UK

25

Table 4: continued

Panel B: Technical Indicators

MA MOM RSI

𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠

2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE

Australia Canada

Euro Japan

New Zealand Norway Sweden

Switzerland UK

26

Table 5: Out-of-Sample Forecasting Results using Recursive Regressions – Developing Countries

The table reports the out-of-sample 𝑅𝑜𝑜𝑠2 , McCracken (2007) MSE-F, and change in RMSE-statistics for the predictive regression models for the developing market

currencies, against the null of a random walk (RW). Panel A shows the results using fundamental indicators given by,

∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑥𝑖𝑡 , ∆�̂�𝑖𝑡+1 is one-step ahead forecast of the log spot exchange rate changes; 𝑥𝑖𝑡 is one of the 3 macroeconomic variables (based on UIP, PPP, and MF). Panel B presents

the results using technical trading indicators given by

∆�̂�𝑖𝑡+1 = �̂� + �̂�𝑧𝑖𝑡

where 𝑧𝑖𝑡 is one of the 3 technical trading signals (based on MA, MOM, and RSI). We obtain the OOS monthly forecasts using rolling regressions using a fixed

window equal to 140 observations. This exercise produces 108 OOS forecasts, so that our first OOS forecast is for September 2008, using IS period from January

1997 to August 2008, and our last OOS forecast is for August 2017, using IS period from August 2006 to July 2017. The last row in each panel shows the estimates

for the pooled sample that includes all the sample currencies. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. NA indicates not

applicable due to data availability during the sample period.

Panel A: Fundamental Indicators

UIP PPP MF

𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠

2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE

Philippine Chez Rep. Hong Kong Indonesia

India Kuwait

Hungary Mexico Saudi

Singapore South Africa

Taiwan Thailand Turkey

27

Table 5: continued

Panel B: Technical Indicators

MA MOM RSI

𝑅𝑜𝑜𝑠2 MSE-F RMSE 𝑅𝑜𝑜𝑠

2 MSE-F RMSE 𝑅𝑜𝑜𝑠2 MSE-F RMSE

Philippine

Chez Rep.

Hong Kong

Indonesia

India

Kuwait

Hungary

Mexico

Saudi

Singapore

South Africa

Taiwan

Thailand

Turkey

28

Table 6: In-Sample Forecasting Results (January 1997 – June 2017)

Under the null hypothesis of no predictability using technical indicators, the table reports the least squares estimates for the following two bivariate predictive

regression model,

∆𝑠𝑖𝑡+1 = 𝛼 + 𝛽𝑥𝑖𝑡 + 𝜀𝑖𝑡+1 ∆𝑠𝑖𝑡+1 = 𝛼 + 𝛽𝑧𝑖𝑡 + 𝜀𝑖𝑡+1

where ∆𝑠𝑖𝑡+1 is the log spot exchange rate changes (i. e., ∆𝑠𝑖𝑡+1 = ln(St+1) − ln(St)); 𝑥𝑖𝑡 is one of the 3 macroeconomic variables (based on UIP, PPP, and MF);

and 𝑧𝑖𝑡 is one of the 3 technical trading signals (based on MA, MOM, and RSI). If currency returns are predictable from fundamental or technical regressors, the

slope coefficient estimate should be insignificantly different from zero. The R2 -statistics are computed for the full estimation period spanning from January 1997

to June 2017 (246 observations). Panel A shows the results for developed market currencies, and Panel B presents the results for emerging market currencies. The

last row in each panel shows the estimates for the pooled sample that includes all the sample currencies. *, ** and *** indicate significance at the 10%, 5% and

1% levels, respectively.

Panel A: Developed Market Currencies

Fundamental Indicators Technical Indicators

UIP PPP MF MA(1,9) MOM RSI

Slope 𝑅2 Slope 𝑅2 Slope 𝑅2 Slope 𝑅2 Slope 𝑅2 Slope 𝑅2

Australia

Canada

Euro

Japan

New Zealand

Norway

Sweden

Switzerland

UK

29

Table 6: continued

Panel B: Emerging Market Currencies

Fundamental Indicators Technical Indicators

UIP PPP MF MA MOM RSI

Slope 𝑅2 Slope 𝑅2 Slope 𝑅2 Slope 𝑅2 Slope 𝑅2 Slope 𝑅2 CZK HKD HUF IDR KRD MXN PHP SAR SGD THB TWD ZAR Pool